




'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase_							:	List[Any]	     =		logging.getLogger(__name__)





@dataclass
class      lowerCAmelCase :
       __lowercase	:   str										=      field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
       __lowercase	:   Optional[str]										=      field(
           default='''NER'''					, metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
       __lowercase	:   bool										=      field(default=__lowerCAmelCase					, metadata={'''help''': '''Set this flag to use fast tokenization.'''})
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					, )





@dataclass
class      lowerCAmelCase :
       __lowercase	:   str										=      field(
           metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					, )
       __lowercase	:   int										=      field(
           default=128					, metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }					, )
       __lowercase	:   bool										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     if len(sys.argv						) == 2 and sys.argv[1].endswith('''.json'''						):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __snake_case       ,     __snake_case       ,     __snake_case            =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __snake_case       ,     __snake_case       ,     __snake_case            =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir						)
         and os.listdir(training_args.output_dir						)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
              ''' --overwrite_output_dir to overcome.'''						)

     __snake_case            =	import_module('''tasks'''						)
     try:
          __snake_case            =	getattr(_lowerCamelCase ,						model_args.task_type						)
          __snake_case            =	token_classification_task_clazz()
     except AttributeError:
          raise ValueError(
              f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
              f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''						)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,						datefmt='''%m/%d/%Y %H:%M:%S''' ,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,						)
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,						training_args.local_rank ,						training_args.device ,						training_args.n_gpu ,						bool(training_args.local_rank != -1						) ,						training_args.fpaa ,						)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank						):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''' ,						_lowerCamelCase						)

     # Set seed
     set_seed(training_args.seed						)

     # Prepare CONLL-2003 task
     __snake_case            =	token_classification_task.get_labels(data_args.labels						)
     __snake_case            =	dict(enumerate(_lowerCamelCase						)						)
     __snake_case            =	len(_lowerCamelCase						)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     __snake_case            =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=_lowerCamelCase ,						idalabel=_lowerCamelCase ,						labelaid={label: i for i, label in enumerate(_lowerCamelCase						)} ,						cache_dir=model_args.cache_dir ,						)
     __snake_case            =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						use_fast=model_args.use_fast ,						)
     __snake_case            =	AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path						) ,						config=_lowerCamelCase ,						cache_dir=model_args.cache_dir ,						)

     # Get datasets
     __snake_case            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.train ,						)
         if training_args.do_train
         else None
     )
     __snake_case            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.dev ,						)
         if training_args.do_eval
         else None
     )

     def align_predictions(_lowerCamelCase				:  np.ndarray ,						_lowerCamelCase				:  np.ndarray						) -> Tuple[List[int], List[int]]:
          __snake_case            =	np.argmax(_lowerCamelCase ,						axis=2						)

          __snake_case       ,     __snake_case            =	preds.shape

          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]

          for i in range(_lowerCamelCase						):
               for j in range(_lowerCamelCase						):
                    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                         out_label_list[i].append(label_map[label_ids[i][j]]						)
                         preds_list[i].append(label_map[preds[i][j]]						)

          return preds_list, out_label_list

     def compute_metrics(_lowerCamelCase				:  EvalPrediction						) -> Dict:
          __snake_case       ,     __snake_case            =	align_predictions(p.predictions ,						p.label_ids						)
          return {
              "accuracy_score": accuracy_score(_lowerCamelCase ,						_lowerCamelCase						),
              "precision": precision_score(_lowerCamelCase ,						_lowerCamelCase						),
              "recall": recall_score(_lowerCamelCase ,						_lowerCamelCase						),
              "f1": fa_score(_lowerCamelCase ,						_lowerCamelCase						),
          }

     # Data collator
     __snake_case            =	DataCollatorWithPadding(_lowerCamelCase ,						pad_to_multiple_of=8						) if training_args.fpaa else None

     # Initialize our Trainer
     __snake_case            =	Trainer(
         model=_lowerCamelCase ,						args=_lowerCamelCase ,						train_dataset=_lowerCamelCase ,						eval_dataset=_lowerCamelCase ,						compute_metrics=_lowerCamelCase ,						data_collator=_lowerCamelCase ,						)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_process_zero():
               tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
     __snake_case            =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''						)

          __snake_case            =	trainer.evaluate()

          __snake_case            =	os.path.join(training_args.output_dir ,						'''eval_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    logger.info('''***** Eval results *****'''						)
                    for key, value in result.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

               results.update(_lowerCamelCase						)

    # Predict
     if training_args.do_predict:
          __snake_case            =	TokenClassificationDataset(
              token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.test ,						)

          __snake_case       ,     __snake_case       ,     __snake_case            =	trainer.predict(_lowerCamelCase						)
          __snake_case       ,     __snake_case            =	align_predictions(_lowerCamelCase ,						_lowerCamelCase						)

          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    for key, value in metrics.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

        # Save predictions
          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_predictions.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    with open(os.path.join(data_args.data_dir ,						'''test.txt'''						) ,						'''r'''						) as f:
                         token_classification_task.write_predictions_to_file(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     return results







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> int:




     '''simple docstring'''





     main()


if __name__ == "__main__":
     main()














'''simple docstring'''





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_UpperCamelCase						:						List[str]  =					logging.getLogger(__name__)

_UpperCamelCase						:						int  =					'pytorch_model.bin'


@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}     ,				)






@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(metadata={"help": "A csv or a json file containing the training data."})
					a_        =					dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "A csv or a json file containing the validation data."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "The name of the task to train on."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "The list of labels for the task."})






@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(
					    metadata={"help": "The output directory where the model predictions and checkpoints will be written."})
					a_        =					dataclasses.field(
					    default="accuracy"     ,				metadata={"help": "The evaluation metric used for the task."})
					a_        =					dataclasses.field(
					    default="no"     ,				metadata={
					        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
					    }     ,				)
					a_        =					dataclasses.field(
					    default=10     ,				metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,				)
					a_        =					dataclasses.field(
					    default=0.0     ,				metadata={
					        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
					    }     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}     ,				)
					a_        =					dataclasses.field(
					    default=0.0     ,				metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}     ,				)
					a_        =					dataclasses.field(
					    default=100     ,				metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Random seed for initialization."}     ,				)






def __UpperCAmelCase   (					A					:			str			,       A					:			Optional[Any]			,       A					:			List[Any]			,       A					:			Any			,       A					:			Union[str, Any]			,       A					:			Dict		)    ->   int:
			UpperCAmelCase_      :       Tuple          =       datasets.concatenate_datasets([infer_input, infer_output]			,       axis=1		)

			if args.do_filter_by_confidence:
						UpperCAmelCase_      :       Tuple          =       dataset.filter(lambda A		: example["probability"] > args.confidence_threshold		)

			if args.do_filter_by_val_performance:
						assert eval_result >= 0.0 and eval_result <= 1.0
						UpperCAmelCase_      :       Dict          =       int(eval_result * len(A		)		)
						print(A		)
						UpperCAmelCase_      :       Optional[int]          =       dataset.sort('''probability'''			,       reverse=A		)
						UpperCAmelCase_      :       Optional[Any]          =       dataset.select(range(A		)		)

			UpperCAmelCase_      :       Dict          =       dataset.remove_columns(['''label''', '''probability''']		)
			UpperCAmelCase_      :       List[Any]          =       dataset.rename_column('''prediction'''			,       '''label'''		)
			UpperCAmelCase_      :       List[str]          =       dataset.map(lambda A		: {"label": idalabel[example["label"]]}		)
			UpperCAmelCase_      :       Union[str, Any]          =       dataset.shuffle(seed=args.seed		)

			UpperCAmelCase_      :       str          =       os.path.join(A			,       F"train_pseudo.{args.data_file_extension}"		)
			if args.data_file_extension == "csv":
						dataset.to_csv(A			,       index=A		)
			else:
						dataset.to_json(A		)







def __UpperCAmelCase   (					A					:			Any			,       A					:			int			,       A					:			Union[str, Any]			,       A					:			Dict			,       **A					:			Any		)    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       Accelerator()
			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,       datefmt='''%m/%d/%Y %H:%M:%S'''			,       level=logging.INFO			,       )
			logger.info(accelerator.state		)

			# Setup logging, we only want one process per machine to log things on the
			# screen. accelerator.is_local_main_process is only True for one process per
			# machine.
			logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_info()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()

			UpperCAmelCase_      :       Tuple          =       STModelArguments(model_name_or_path=A		)
			UpperCAmelCase_      :       int          =       STDataArguments(train_file=A			,       infer_file=A		)
			UpperCAmelCase_      :       Optional[Any]          =       STTrainingArguments(output_dir=A		)
			UpperCAmelCase_      :       Optional[int]          =       argparse.Namespace()

			for arg_class in (model_args, data_args, training_args):
						for key, value in vars(A		).items():
									setattr(A			,       A			,       A		)

			for key, value in kwargs.items():
						if hasattr(A			,       A		):
									setattr(A			,       A			,       A		)

    # Sanity checks
			UpperCAmelCase_      :       List[str]          =       {}
			UpperCAmelCase_      :       Any          =       None

			# You need to provide the training data and the data to predict on
			assert args.train_file is not None
			assert args.infer_file is not None
			UpperCAmelCase_      :       List[Any]          =       args.train_file
			UpperCAmelCase_      :       Optional[int]          =       args.infer_file

			if args.evaluation_strategy != IntervalStrategy.NO.value:
						assert args.eval_file is not None
						UpperCAmelCase_      :       List[str]          =       args.eval_file

			for key in data_files:
						UpperCAmelCase_      :       Dict          =       data_files[key].split('''.'''		)[-1]
						assert extension in ["csv", "json"], F"`{key}_file` should be a csv or a json file."
						if args.data_file_extension is None:
									UpperCAmelCase_      :       Any          =       extension
						else:
									assert extension == args.data_file_extension, F"`{key}_file` should be a {args.data_file_extension} file`."

			assert (
			    args.eval_metric in datasets.list_metrics()
			), F"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."

			# If passed along, set the training seed now.
			if args.seed is not None:
						set_seed(args.seed		)

			logger.info('''Creating the initial data directory for self-training...'''		)
			UpperCAmelCase_      :       Any          =       F"{args.output_dir}/self-train_iter-{{}}".format
			UpperCAmelCase_      :       Any          =       data_dir_format(0		)

			if accelerator.is_main_process:
						if args.output_dir is not None:
									os.makedirs(args.output_dir			,       exist_ok=A		)
									os.makedirs(A			,       exist_ok=A		)
			accelerator.wait_for_everyone()

			UpperCAmelCase_      :       Union[str, Any]          =       None
			UpperCAmelCase_      :       Tuple          =       None
			UpperCAmelCase_      :       Optional[Any]          =       0
			UpperCAmelCase_      :       int          =       False
			# Show the progress bar
			UpperCAmelCase_      :       int          =       tqdm(range(args.max_selftrain_iterations		)			,       disable=not accelerator.is_local_main_process		)

			# Self-train
			for iteration in range(0			,       int(args.max_selftrain_iterations		)		):
						UpperCAmelCase_      :       str          =       data_dir_format(A		)
						assert os.path.exists(A		)

						# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
						# iteration > 0
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''stage-1'''		)
						UpperCAmelCase_      :       Dict          =       {
						    '''accelerator''': accelerator,
						    '''model_name_or_path''': args.model_name_or_path,
						    '''cache_dir''': args.cache_dir,
						    '''do_train''': True,
						    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
						    '''do_eval''': True if args.eval_file is not None else False,
						    '''eval_file''': data_files['''eval'''],
						    '''do_predict''': True,
						    '''infer_file''': data_files['''infer'''],
						    '''task_name''': args.task_name,
						    '''label_list''': args.label_list,
						    '''output_dir''': current_output_dir,
						    '''eval_metric''': args.eval_metric,
						    '''evaluation_strategy''': args.evaluation_strategy,
						    '''early_stopping_patience''': args.early_stopping_patience,
						    '''early_stopping_threshold''': args.early_stopping_threshold,
						    '''seed''': args.seed,
						}
						# Add additional training arguments
						for key, value in kwargs.items():
									if key not in arguments_dict and not hasattr(A			,       A		):
												arguments_dict.update({key: value}		)

						UpperCAmelCase_      :       int          =       os.path.join(A			,       '''best-checkpoint'''			,       A		)
						if os.path.exists(A		):
									logger.info(
									    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''			,       A			,       A			,       )
						else:
									logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''			,       A		)
									finetune(**A		)
									accelerator.wait_for_everyone()
									assert os.path.exists(A		)
									logger.info('''Self-training job completed: iteration: %d, stage: 1.'''			,       A		)

						if iteration > 0 and args.finetune_on_labeled_data:
									# Stage 2 (optional): fine-tuning on the original labeled data
									UpperCAmelCase_      :       Any          =       os.path.join(A			,       '''best-checkpoint'''		)
									UpperCAmelCase_      :       Optional[int]          =       os.path.join(A			,       '''stage-2'''		)
									# Update arguments_dict
									UpperCAmelCase_      :       List[Any]          =       model_path
									UpperCAmelCase_      :       Optional[int]          =       data_files['''train''']
									UpperCAmelCase_      :       Union[str, Any]          =       current_output_dir

									UpperCAmelCase_      :       Optional[int]          =       os.path.join(A			,       '''best-checkpoint'''			,       A		)
									if os.path.exists(A		):
												logger.info(
												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''			,       A			,       A			,       )
									else:
												logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''			,       A		)
												finetune(**A		)
												accelerator.wait_for_everyone()
												assert os.path.exists(A		)
												logger.info('''Self-training job completed: iteration: %d, stage: 2.'''			,       A		)

						UpperCAmelCase_      :       Dict          =       iteration
						UpperCAmelCase_      :       Optional[Any]          =       data_dir_format(iteration + 1		)

						UpperCAmelCase_      :       Any          =       AutoConfig.from_pretrained(os.path.join(A			,       '''best-checkpoint'''		)		)
						UpperCAmelCase_      :       Optional[Any]          =       config.idalabel
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''eval_results_best-checkpoint.json'''		)
						UpperCAmelCase_      :       Union[str, Any]          =       os.path.join(A			,       '''test_results_best-checkpoint.json'''		)
						assert os.path.exists(A		)

						with open(A			,       '''r'''		) as f:
									UpperCAmelCase_      :       Optional[int]          =       float(json.load(A		)[args.eval_metric]		)
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''infer_output_best-checkpoint.csv'''		)
						assert os.path.exists(A		)
						# Loading the dataset from local csv or json files.
						UpperCAmelCase_      :       str          =       load_dataset(args.data_file_extension			,       data_files={'''data''': data_files['''infer''']}		)['''data''']
						UpperCAmelCase_      :       Union[str, Any]          =       load_dataset('''csv'''			,       data_files={'''data''': infer_output_file}		)['''data''']

						if accelerator.is_main_process:
									os.makedirs(A			,       exist_ok=A		)
									shutil.copy(A			,       os.path.join(A			,       F"eval_results_iter-{iteration}.json"		)		)
									if os.path.exists(A		):
												shutil.copy(A			,       os.path.join(A			,       F"test_results_iter-{iteration}.json"		)		)
									create_pseudo_labeled_data(A			,       A			,       A			,       A			,       A			,       A		)
						accelerator.wait_for_everyone()

						UpperCAmelCase_      :       int          =       os.path.join(A			,       F"train_pseudo.{args.data_file_extension}"		)

						if args.evaluation_strategy != IntervalStrategy.NO.value:
									UpperCAmelCase_      :       List[str]          =       eval_result

									if best_iteration is None:
												UpperCAmelCase_      :       Any          =       new_iteration
												UpperCAmelCase_      :       List[Any]          =       new_eval_result
									else:
												if new_eval_result - best_eval_result > args.early_stopping_threshold:
															UpperCAmelCase_      :       str          =       new_iteration
															UpperCAmelCase_      :       Optional[int]          =       new_eval_result
															UpperCAmelCase_      :       Any          =       0
												else:
															if new_eval_result == best_eval_result:
																		UpperCAmelCase_      :       int          =       new_iteration
																		UpperCAmelCase_      :       List[Any]          =       new_eval_result
															early_stopping_patience_counter += 1

												if early_stopping_patience_counter >= args.early_stopping_patience:
															UpperCAmelCase_      :       List[Any]          =       True

						progress_bar.update(1		)

						if should_training_stop:
									break

			if best_iteration is not None:
						# Save the best iteration
						logger.info('''Best iteration: %d'''			,       A		)
						logger.info('''Best evaluation result: %s = %f'''			,       args.eval_metric			,       A		)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(A			,       F"eval_results_iter-{iteration}.json"		)			,       os.path.join(A			,       '''eval_results_best-iteration.json'''		)			,       )
			else:
						# Assume that the last iteration is the best
						logger.info('''Best iteration: %d'''			,       args.max_selftrain_iterations - 1		)
						logger.info('''Best evaluation result: %s = %f'''			,       args.eval_metric			,       A		)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(A			,       F"eval_results_iter-{args.max_selftrain_iterations - 1}.json"		)			,       os.path.join(A			,       '''eval_results_best-iteration.json'''		)			,       )


